Introduction
Control and digital platforms integrate data and AI technologies to enable real-time monitoring, coordinated control, and intelligent optimization of electrochemical systems. By building system-level digital twin models, physical systems are mapped to digital counterparts, enabling visualization, predictability, and optimization of operational status. Through unified control and data analysis across electrolysis, gas-liquid treatment, thermal management, and energy systems, the system not only operates stably but also adapts and optimizes under complex conditions, providing intelligent support for scaled deployment and long-term operation.
Key Functions
System control and automationSystem Control & Automation
Introduction
System control and automation are used to achieve stable operation and coordinated performance of electrochemical systems under varying conditions, forming the foundation for safety and continuous operation. Through unified control of electrolysis units, gas-liquid treatment, thermal management, and auxiliary systems, coordinated operation across multiple modules is enabled. In complex operating scenarios and multi-pathway coupling applications, the control system provides real-time regulation and response of key parameters, ensuring stability, consistency, and controllability, while laying the groundwork for data analysis and intelligent optimization.
Key Functions
Automated control systemData Acquisition & Real-time Monitoring
Introduction
Data acquisition and real-time monitoring are used to continuously capture and visualize key parameters during electrochemical system operation, forming the foundation for control, optimization, and intelligent operation. By monitoring current, voltage, temperature, pressure, flow, and other variables, a comprehensive operational data set is established. In multi-module and complex operating scenarios, the data system reflects system status and trends, supporting control strategy adjustment, performance evaluation, and subsequent intelligent optimization, transforming the system from "operable" to "understandable."
Key Functions
Multi-parameter data acquisitionAI-Driven Optimization & Digital Twin
Introduction
AI-driven optimization and digital twin technologies enable data-driven performance improvement and operational prediction during system operation, representing a key step in advancing electrochemical systems from "controllable" to "intelligent" operation. Based on system operational data and mechanistic models, we build digital twin representations of the system to simulate and predict operating states. AI methods are then applied to continuously optimize operational strategies. Under complex conditions and multi-system coupling scenarios, this approach shifts system control from reactive to proactive optimization, enhancing overall efficiency and stability.
Key Functions
Operational data modeling and analysis|
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